In this work, we develop a water demand prediction model for MPC that reliably handles unexpected changes from the daily pattern by incorporating a dynamical model over the current measured demand, fitted using machine learning methods. Secondly, in alignment with the new demand estimator, we also propose a multi-resolution MPC prediction horizon. This improves the responsiveness to unforeseeable disturbances with minimal impact on computational efficiency. Read more here: https://doi.org/10.3390/engproc2024069070
